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The African swine fever modelling challenge: Objectives, model description and synthetic data generation

Abstract : Highlights • The 1st modelling challenge in animal health mimics ASF spread with synthetic data. • A mechanistic spatially explicit stochastic model was developed to represent ASF spread and control. • Synthetic data concerned date and location of detected cases in pigs and wild boar. • Modelling ASF at the wildlife-livestock interface illustrates the reservoir role of wild fauna. Abstract African swine fever (ASF) is an emerging disease currently spreading at the interface between wild boar and pig farms in Europe and Asia. Current disease control regulations, which involve massive culling with significant economic and animal welfare costs, need to be improved. Modelling enables relevant control measures to be explored, but conducting the exercise during an epidemic is extremely difficult. Modelling challenges enhance modellers’ ability to timely advice policy makers, improve their readiness when facing emerging threats, and promote international collaborations. The ASF-Challenge, which ran between August 2020 and January 2021, was the first modelling challenge in animal health. In this paper, we describe the objectives and rules of the challenge. We then demonstrate the mechanistic multi-host model that was used to mimic as accurately as possible an ASF-like epidemic, provide a detailed explanation of the surveillance and intervention strategies that generated the synthetic data, and describe the different management strategies that were assessed by the competing modelling teams. We then outline the different technical steps of the challenge as well as its environment. Finally, we synthesize the lessons we learnt along the way to guide future modelling challenges in animal health.
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Submitted on : Friday, August 12, 2022 - 5:11:44 PM
Last modification on : Friday, September 2, 2022 - 11:04:28 AM


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Sébastien Picault, Timothée Vergne, Matthieu Mancini, Servane Bareille, Pauline Ezanno. The African swine fever modelling challenge: Objectives, model description and synthetic data generation. Epidemics, Elsevier, 2022, 40, pp.100616. ⟨10.1016/j.epidem.2022.100616⟩. ⟨hal-03739528⟩



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